{"title":"开发诊断性单克隆抗体的挑战与突破","authors":"Jing Wang, Qitao Song, Tao Yang, Yuanli Li, Lihua Zhang, Jiayan Li, Feifei Liu, Yanyin Lin, Xiaoxia Xu, Yu Heng, Lulai Xu, Shun Zhang, Jiahui Zhou, Yunbo Liu, Lingyuan Kong, Dingbin Tang, Chengdong Ji, Bing Tan, Pu Liao, Nengke Pan, Weijing Yi, Zhanhui Wang","doi":"10.1002/viw.20240017","DOIUrl":null,"url":null,"abstract":"Over the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of artificial intelligence-based antibody generation and the often overlooked non-animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high-quality antigen formulations. This journey has traversed various stages, from methods involving serum-based antibody acquisition, the isolation of B cells capable of perpetual antibody production through hybridoma technology, to the in-depth exploration of genetic material using the phage display system, and the current stage involving diverse single B cell screening techniques. Additionally, the emergence of machine learning has brought impressive scientific and technological breakthroughs across research domains, proving to be a powerful application in the field of antibody discovery. However, each technique comes with its limitations, such as variability and control challenges in serum-based acquisition, lengthy and difficult hybridoma-derived antibody development, potential limitations in sequence and epitope diversity due to immunization biases in phage display techniques, and costly single B cell screening. Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. Aimed at moving closer to the gold standard of antibody discovery, these strategies enhance the competitiveness of diagnostic reagent products.","PeriodicalId":34127,"journal":{"name":"VIEW","volume":"28 1","pages":""},"PeriodicalIF":9.7000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The challenges and breakthroughs in the development of diagnostic monoclonal antibodies\",\"authors\":\"Jing Wang, Qitao Song, Tao Yang, Yuanli Li, Lihua Zhang, Jiayan Li, Feifei Liu, Yanyin Lin, Xiaoxia Xu, Yu Heng, Lulai Xu, Shun Zhang, Jiahui Zhou, Yunbo Liu, Lingyuan Kong, Dingbin Tang, Chengdong Ji, Bing Tan, Pu Liao, Nengke Pan, Weijing Yi, Zhanhui Wang\",\"doi\":\"10.1002/viw.20240017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Over the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of artificial intelligence-based antibody generation and the often overlooked non-animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high-quality antigen formulations. 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Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. 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引用次数: 0
摘要
在过去的一个世纪中,抗体发现领域经历了重大演变,其中不包括目前基于人工智能的抗体生成探索阶段,以及经常被忽视的非动物来源抗体发现,后者通常需要成熟的体外亲和力和高质量抗原配方的选择。这一历程经历了多个阶段,从基于血清的抗体获取方法,到通过杂交瘤技术分离出能够永久产生抗体的 B 细胞,再到利用噬菌体展示系统对遗传物质进行深入探索,以及目前涉及多种单 B 细胞筛选技术的阶段。此外,机器学习的出现为各研究领域带来了令人印象深刻的科技突破,被证明是抗体发现领域的一项强大应用。然而,每种技术都有其局限性,如基于血清的采集存在变异性和控制挑战、杂交瘤衍生抗体的开发漫长而困难、噬菌体展示技术的免疫偏差可能导致序列和表位多样性的限制,以及单 B 细胞筛选成本高昂。蛋白质质谱测序采集时间短、成本低,被诊断公司视为捷径,对传统抗体开发造成冲击。在诊断性抗体开发过程中,下游检测方法的差异和 Fv 核心外恒定区的影响往往被忽视。本文深入分析了面临的挑战,提出了新一代诊断抗体开发的创新策略。这些策略旨在向抗体发现的黄金标准靠拢,提高诊断试剂产品的竞争力。
The challenges and breakthroughs in the development of diagnostic monoclonal antibodies
Over the past century, the field of antibody discovery has undergone significant evolution, excluding the current exploration stage of artificial intelligence-based antibody generation and the often overlooked non-animal sourced antibody discovery, which typically requires mature in vitro affinity and the selection of high-quality antigen formulations. This journey has traversed various stages, from methods involving serum-based antibody acquisition, the isolation of B cells capable of perpetual antibody production through hybridoma technology, to the in-depth exploration of genetic material using the phage display system, and the current stage involving diverse single B cell screening techniques. Additionally, the emergence of machine learning has brought impressive scientific and technological breakthroughs across research domains, proving to be a powerful application in the field of antibody discovery. However, each technique comes with its limitations, such as variability and control challenges in serum-based acquisition, lengthy and difficult hybridoma-derived antibody development, potential limitations in sequence and epitope diversity due to immunization biases in phage display techniques, and costly single B cell screening. Protein mass spectrometry sequencing, with shorter acquisition time and lower costs, is seen as a shortcut by diagnostic companies, impacting traditional antibody development. In diagnostic antibody development, methodological differences in downstream assays and the impact of constant regions outside the Fv core are often neglected. This paper deeply analyzes challenges, proposing innovative strategies for the next generation of diagnostic antibody development. Aimed at moving closer to the gold standard of antibody discovery, these strategies enhance the competitiveness of diagnostic reagent products.
期刊介绍:
View publishes scientific articles studying novel crucial contributions in the areas of Biomaterials and General Chemistry. View features original academic papers which go through peer review by experts in the given subject area.View encourages submissions from the research community where the priority will be on the originality and the practical impact of the reported research.